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The t-test has many assumptions. That dataset violates several of them: Data should be sufficiently large (>30 independent points) Data should be approximately normally distributed Given that the …

One approach is analysis of variance (ANOVA) which is a framework for analyzing mean differences while controlling for those effects. Typically a mixed-effect model is applied that handles experimenta …

One option is a permutation test. A permutation does not make any assumptions about the distribution of the data and allows for testing maximum change. For a permutation test, you randomly assign da …

Another method is Bayesian Blocks from Studies in Astronomical Time Series Analysis. VI. Bayesian Block Representations by Scargle et al. Bayesian Blocks is a dynamic histogramming method which optim …

One way to approach is the model conversion is survival analysis. In this case, time-to-event would be how many ads until they convert.

If 50% or more of the values in a sample are identical, you'll have to switch to alternative method from MAD or drop values. "Alternatives to the Median Absolute Deviation" paper by Rousseeuw and Crou …

The unlabeled data will have to be labeled to calculate accuracy. How much data to label is an empirical question. Here are questions to answer to help guide that process: How close is the distributi …

One option is constructing a contingency table, aka crosstab, which displays the frequency distribution of categorical variables. From a contingency table, different measures of association can be fou …

The normal distribution is a theoretical model of data. Empirical data can be distributed more similarily or more dissimilarly to a normal distribution. That empirical data has a couple of notable di …

That is commonly called an index, an accumulation of scores from a variety of individual items. Creating indexes is very common in economic and financial fields (e.g., Consumer Purchasing Index and St …

There are two separate issues: Sampling - Picking the optional ingredient level for next experiment to run. Given you have only have 4 explanatory variables, just plot them. Either all pairwise or …

One option would be look at conditional probability based on percentiles. First, find percentiles based on all the data. For example, 99th percentile is 1432 milliseconds. Then, find percent of a spec …

One option is to plot fewer epochs. There is no useful information after 20 epochs because training and validation performance are the same after that point.

Combing multiple models together is called typically ensembling. You are describing hierarchical ensembling. There are simpler methods for ensembling, such as voting and weighted voting.

In hierarchical clustering, both agglomerative and divisive, you do not have to pre-specify the number of clusters. You can create all possible clusters and then select the number cluster to use at th …